Overview

Dataset statistics

Number of variables11
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.6 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

storm has constant value ""Constant
storm phase has constant value ""Constant
400kmDensity is highly overall correlated with SYM/H_INDEX_nT and 4 other fieldsHigh correlation
SYM/H_INDEX_nT is highly overall correlated with 400kmDensityHigh correlation
1-M_AE_nT is highly overall correlated with SYM/H_INDEX_nTHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with DAILY_SUNSPOT_NO_ and 3 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_SUNSPOT_NO_ has 195143 (19.5%) zerosZeros
d_diff has 17111 (1.7%) zerosZeros

Reproduction

Analysis started2023-02-24 21:41:31.602478
Analysis finished2023-02-24 21:42:19.252315
Duration47.65 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

400kmDensity
Real number (ℝ)

Distinct928244
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7697734 × 10-12
Minimum7.457587 × 10-20
Maximum2.63181 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:19.333069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7.457587 × 10-20
5-th percentile2.9380307 × 10-13
Q16.76516 × 10-13
median1.252484 × 10-12
Q32.359647 × 10-12
95-th percentile5.0406663 × 10-12
Maximum2.63181 × 10-11
Range2.63181 × 10-11
Interquartile range (IQR)1.683131 × 10-12

Descriptive statistics

Standard deviation1.553036 × 10-12
Coefficient of variation (CV)0.8775338
Kurtosis0
Mean1.7697734 × 10-12
Median Absolute Deviation (MAD)7.0422885 × 10-13
Skewness0
Sum1.7697734 × 10-6
Variance2.4119209 × 10-24
MonotonicityNot monotonic
2023-02-24T16:42:19.468735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.032226 × 10-126
 
< 0.1%
1.136118 × 10-126
 
< 0.1%
1.379522 × 10-125
 
< 0.1%
1.145916 × 10-125
 
< 0.1%
1.23369 × 10-125
 
< 0.1%
1.035372 × 10-125
 
< 0.1%
1.57206 × 10-125
 
< 0.1%
1.106253 × 10-125
 
< 0.1%
1.042851 × 10-125
 
< 0.1%
1.952615 × 10-125
 
< 0.1%
Other values (928234) 999948
> 99.9%
ValueCountFrequency (%)
7.457587 × 10-201
< 0.1%
2.832052 × 10-161
< 0.1%
1.174258 × 10-151
< 0.1%
1.348471 × 10-151
< 0.1%
1.517717 × 10-151
< 0.1%
1.548175 × 10-151
< 0.1%
1.576348 × 10-151
< 0.1%
1.659292 × 10-151
< 0.1%
1.709332 × 10-151
< 0.1%
1.863495 × 10-151
< 0.1%
ValueCountFrequency (%)
2.63181 × 10-111
< 0.1%
2.524422 × 10-111
< 0.1%
2.523852 × 10-111
< 0.1%
2.504972 × 10-111
< 0.1%
2.488264 × 10-111
< 0.1%
2.255126 × 10-111
< 0.1%
2.217619 × 10-111
< 0.1%
2.180779 × 10-111
< 0.1%
2.163607 × 10-111
< 0.1%
2.161917 × 10-111
< 0.1%

SYM/H_INDEX_nT
Real number (ℝ)

Distinct474
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-21.13251
Minimum-431
Maximum66
Zeros8956
Zeros (%)0.9%
Negative935860
Negative (%)93.6%
Memory size15.3 MiB
2023-02-24T16:42:19.593401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-431
5-th percentile-55
Q1-28
median-17
Q3-9
95-th percentile1
Maximum66
Range497
Interquartile range (IQR)19

Descriptive statistics

Standard deviation21.477742
Coefficient of variation (CV)-1.0163365
Kurtosis40.588882
Mean-21.13251
Median Absolute Deviation (MAD)9
Skewness-4.0797823
Sum-21132510
Variance461.2934
MonotonicityNot monotonic
2023-02-24T16:42:19.714051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-15 32962
 
3.3%
-13 32331
 
3.2%
-14 32285
 
3.2%
-16 32217
 
3.2%
-17 31263
 
3.1%
-12 31239
 
3.1%
-18 31007
 
3.1%
-11 30623
 
3.1%
-10 30189
 
3.0%
-19 30149
 
3.0%
Other values (464) 685735
68.6%
ValueCountFrequency (%)
-431 1
 
< 0.1%
-429 2
< 0.1%
-425 2
< 0.1%
-424 1
 
< 0.1%
-423 1
 
< 0.1%
-421 3
< 0.1%
-418 1
 
< 0.1%
-417 1
 
< 0.1%
-415 2
< 0.1%
-413 2
< 0.1%
ValueCountFrequency (%)
66 1
 
< 0.1%
62 2
< 0.1%
61 1
 
< 0.1%
59 1
 
< 0.1%
57 3
< 0.1%
56 3
< 0.1%
55 3
< 0.1%
54 1
 
< 0.1%
53 2
< 0.1%
52 1
 
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct2093
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.89519
Minimum1
Maximum3568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:19.837720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q161
median155
Q3334
95-th percentile697
Maximum3568
Range3567
Interquartile range (IQR)273

Descriptive statistics

Standard deviation232.48287
Coefficient of variation (CV)0.99396174
Kurtosis5.8157435
Mean233.89519
Median Absolute Deviation (MAD)110
Skewness1.9150233
Sum2.3389519 × 108
Variance54048.283
MonotonicityNot monotonic
2023-02-24T16:42:19.967403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 6033
 
0.6%
34 6004
 
0.6%
33 5990
 
0.6%
38 5966
 
0.6%
35 5943
 
0.6%
36 5882
 
0.6%
31 5872
 
0.6%
32 5795
 
0.6%
30 5742
 
0.6%
39 5715
 
0.6%
Other values (2083) 941058
94.1%
ValueCountFrequency (%)
1 13
 
< 0.1%
2 68
 
< 0.1%
3 189
 
< 0.1%
4 332
 
< 0.1%
5 467
 
< 0.1%
6 686
0.1%
7 901
0.1%
8 1130
0.1%
9 1473
0.1%
10 1714
0.2%
ValueCountFrequency (%)
3568 1
< 0.1%
3534 1
< 0.1%
3513 1
< 0.1%
3505 1
< 0.1%
3481 1
< 0.1%
3441 1
< 0.1%
3437 1
< 0.1%
3413 1
< 0.1%
3360 1
< 0.1%
3358 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct193
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.218569
Minimum0
Maximum281
Zeros195143
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:20.101818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median43
Q384
95-th percentile157
Maximum281
Range281
Interquartile range (IQR)71

Descriptive statistics

Standard deviation52.861701
Coefficient of variation (CV)0.95731747
Kurtosis1.1059169
Mean55.218569
Median Absolute Deviation (MAD)32
Skewness1.1460364
Sum55218569
Variance2794.3594
MonotonicityNot monotonic
2023-02-24T16:42:20.216507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 195143
 
19.5%
13 24058
 
2.4%
12 19807
 
2.0%
15 19140
 
1.9%
18 15627
 
1.6%
16 14697
 
1.5%
61 13302
 
1.3%
11 12827
 
1.3%
58 12003
 
1.2%
33 11908
 
1.2%
Other values (183) 661488
66.1%
ValueCountFrequency (%)
0 195143
19.5%
6 388
 
< 0.1%
7 919
 
0.1%
9 2441
 
0.2%
10 4794
 
0.5%
11 12827
 
1.3%
12 19807
 
2.0%
13 24058
 
2.4%
14 9467
 
0.9%
15 19140
 
1.9%
ValueCountFrequency (%)
281 817
0.1%
279 885
0.1%
267 883
0.1%
263 807
0.1%
250 80
 
< 0.1%
248 900
0.1%
247 690
0.1%
239 908
0.1%
234 296
 
< 0.1%
226 911
0.1%

DAILY_F10.7_
Real number (ℝ)

Distinct702
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.91132
Minimum65.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:20.345162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.1
5-th percentile67.9
Q174
median90.2
Q3118.5
95-th percentile167.7
Maximum999.9
Range934.8
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation72.998492
Coefficient of variation (CV)0.69581139
Kurtosis117.93697
Mean104.91132
Median Absolute Deviation (MAD)19.3
Skewness9.9208284
Sum1.0491132 × 108
Variance5328.7798
MonotonicityNot monotonic
2023-02-24T16:42:20.469829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.8 8083
 
0.8%
67.4 7605
 
0.8%
68 7075
 
0.7%
69.1 6596
 
0.7%
76.1 6367
 
0.6%
70.8 6319
 
0.6%
69.3 6142
 
0.6%
70.5 5913
 
0.6%
69.6 5736
 
0.6%
68.7 5578
 
0.6%
Other values (692) 934586
93.5%
ValueCountFrequency (%)
65.1 914
 
0.1%
66.1 324
 
< 0.1%
66.2 3458
0.3%
66.3 1945
0.2%
66.4 1286
 
0.1%
66.5 840
 
0.1%
66.6 2228
0.2%
66.7 2669
0.3%
66.8 1513
0.2%
66.9 934
 
0.1%
ValueCountFrequency (%)
999.9 5346
0.5%
267.6 80
 
< 0.1%
245.2 908
 
0.1%
232.3 817
 
0.1%
229.5 934
 
0.1%
225.1 923
 
0.1%
225 672
 
0.1%
223.5 353
 
< 0.1%
219.1 807
 
0.1%
215.7 885
 
0.1%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct1014
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0069685044
Minimum0.005898
Maximum0.00972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:20.590503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.005898
5-th percentile0.005996
Q10.0063
median0.006801
Q30.007503
95-th percentile0.008541
Maximum0.00972
Range0.003822
Interquartile range (IQR)0.001203

Descriptive statistics

Standard deviation0.00080734281
Coefficient of variation (CV)0.11585597
Kurtosis-0.097049713
Mean0.0069685044
Median Absolute Deviation (MAD)0.000559
Skewness0.77232588
Sum6968.5044
Variance6.5180242 × 10-7
MonotonicityNot monotonic
2023-02-24T16:42:20.712186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006659 4526
 
0.5%
0.006284 3644
 
0.4%
0.006479 3617
 
0.4%
0.005991 3453
 
0.3%
0.005961 3422
 
0.3%
0.006016 3160
 
0.3%
0.00598 3143
 
0.3%
0.007846 2937
 
0.3%
0.006008 2862
 
0.3%
0.006024 2765
 
0.3%
Other values (1004) 966471
96.6%
ValueCountFrequency (%)
0.005898 922
0.1%
0.005908 913
0.1%
0.005912 874
0.1%
0.005918 888
0.1%
0.00594 894
0.1%
0.005941 922
0.1%
0.005942 911
0.1%
0.005946 1785
0.2%
0.005948 925
0.1%
0.00595 940
0.1%
ValueCountFrequency (%)
0.00972 672
0.1%
0.009581 923
0.1%
0.009577 817
0.1%
0.009483 807
0.1%
0.009459 885
0.1%
0.009429 353
 
< 0.1%
0.009374 883
0.1%
0.009366 910
0.1%
0.009351 934
0.1%
0.009242 878
0.1%
Distinct1077
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26915858
Minimum0.26299
Maximum0.28426999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:20.847819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26299
5-th percentile0.26372999
Q10.26506999
median0.26807001
Q30.27241999
95-th percentile0.27816001
Maximum0.28426999
Range0.02127999
Interquartile range (IQR)0.00735

Descriptive statistics

Standard deviation0.0046589376
Coefficient of variation (CV)0.017309266
Kurtosis-0.28169319
Mean0.26915858
Median Absolute Deviation (MAD)0.00342002
Skewness0.74750019
Sum269158.58
Variance2.1705699 × 10-5
MonotonicityNot monotonic
2023-02-24T16:42:20.973455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26807001 4442
 
0.4%
0.26567999 4371
 
0.4%
0.26418999 4209
 
0.4%
0.26565999 3679
 
0.4%
0.26365 3678
 
0.4%
0.26434001 3624
 
0.4%
0.26464 3124
 
0.3%
0.26363999 3041
 
0.3%
0.27026001 2954
 
0.3%
0.26396 2890
 
0.3%
Other values (1067) 963988
96.4%
ValueCountFrequency (%)
0.26299 324
 
< 0.1%
0.26312 932
0.1%
0.26313001 924
0.1%
0.26315001 1836
0.2%
0.26317999 533
 
0.1%
0.26319 38
 
< 0.1%
0.26320001 906
0.1%
0.26321 1067
0.1%
0.26322001 901
0.1%
0.26323 1836
0.2%
ValueCountFrequency (%)
0.28426999 672
0.1%
0.2841 817
0.1%
0.28373 353
 
< 0.1%
0.28360999 910
0.1%
0.28347999 807
0.1%
0.2834 885
0.1%
0.28323999 923
0.1%
0.28264999 883
0.1%
0.28233999 878
0.1%
0.28209001 934
0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct1371
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0056144003
Minimum0.0048855622
Maximum0.0073493496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:42:21.098151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048855622
5-th percentile0.0049325922
Q10.0051508932
median0.0055226702
Q30.0060089058
95-th percentile0.0067048087
Maximum0.0073493496
Range0.0024637873
Interquartile range (IQR)0.00085801259

Descriptive statistics

Standard deviation0.00056117565
Coefficient of variation (CV)0.099952912
Kurtosis-0.35857924
Mean0.0056144003
Median Absolute Deviation (MAD)0.00040193135
Skewness0.69863994
Sum5614.4003
Variance3.1491812 × 10-7
MonotonicityNot monotonic
2023-02-24T16:42:21.220822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00498051988 1853
 
0.2%
0.004955403507 1834
 
0.2%
0.004922427703 1827
 
0.2%
0.004939651117 1449
 
0.1%
0.004946734291 1439
 
0.1%
0.005749352276 964
 
0.1%
0.006068442017 960
 
0.1%
0.005742751062 958
 
0.1%
0.006868954282 956
 
0.1%
0.005069345236 956
 
0.1%
Other values (1361) 986804
98.7%
ValueCountFrequency (%)
0.00488556223 869
0.1%
0.004886395764 528
0.1%
0.004887722898 40
 
< 0.1%
0.004894145299 891
0.1%
0.00489506498 891
0.1%
0.004898893181 909
0.1%
0.004901738372 439
< 0.1%
0.004901985172 925
0.1%
0.004902268294 916
0.1%
0.004902670626 428
< 0.1%
ValueCountFrequency (%)
0.007349349558 235
 
< 0.1%
0.007334709167 899
0.1%
0.007266042288 905
0.1%
0.007257604506 350
 
< 0.1%
0.007208690513 851
0.1%
0.007195423823 855
0.1%
0.007172006648 97
 
< 0.1%
0.007170669734 828
0.1%
0.00715208007 944
0.1%
0.00715182675 901
0.1%

storm
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
1
1000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000000
100.0%

Length

2023-02-24T16:42:21.324517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:42:21.413307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000000
100.0%

storm phase
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
2
1000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1000000
100.0%

Length

2023-02-24T16:42:21.489105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:42:21.580860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1000000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1000000
100.0%

d_diff
Real number (ℝ)

Distinct811983
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0184074 × 10-16
Minimum-1.1317823 × 10-11
Maximum9.455553 × 10-12
Zeros17111
Zeros (%)1.7%
Negative479395
Negative (%)47.9%
Memory size15.3 MiB
2023-02-24T16:42:21.682594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1317823 × 10-11
5-th percentile-2.027293 × 10-13
Q1-4.3686775 × 10-14
median4.979 × 10-16
Q34.6091 × 10-14
95-th percentile1.9506311 × 10-13
Maximum9.455553 × 10-12
Range2.0773376 × 10-11
Interquartile range (IQR)8.9777775 × 10-14

Descriptive statistics

Standard deviation2.0878355 × 10-13
Coefficient of variation (CV)-1034.3975
Kurtosis0
Mean-2.0184074 × 10-16
Median Absolute Deviation (MAD)4.49199 × 10-14
Skewness0
Sum-2.0184074 × 10-10
Variance4.3590569 × 10-26
MonotonicityNot monotonic
2023-02-24T16:42:21.805260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17111
 
1.7%
3.1812 × 10-1411
 
< 0.1%
-1.2888 × 10-1410
 
< 0.1%
1.1679 × 10-1410
 
< 0.1%
-2.847 × 10-159
 
< 0.1%
-2.896 × 10-159
 
< 0.1%
-1.91 × 10-169
 
< 0.1%
-1.5376 × 10-149
 
< 0.1%
2.4182 × 10-149
 
< 0.1%
3.3986 × 10-149
 
< 0.1%
Other values (811973) 982804
98.3%
ValueCountFrequency (%)
-1.1317823 × 10-111
< 0.1%
-9.03666 × 10-121
< 0.1%
-8.2904156 × 10-121
< 0.1%
-8.118368 × 10-121
< 0.1%
-7.847266 × 10-121
< 0.1%
-7.8236402 × 10-121
< 0.1%
-7.45256303 × 10-121
< 0.1%
-7.065111 × 10-121
< 0.1%
-7.0565975 × 10-121
< 0.1%
-6.741441 × 10-121
< 0.1%
ValueCountFrequency (%)
9.455553 × 10-121
< 0.1%
9.3426776 × 10-121
< 0.1%
8.8378835 × 10-121
< 0.1%
8.5252 × 10-121
< 0.1%
7.0319074 × 10-121
< 0.1%
6.994028 × 10-121
< 0.1%
6.9862178 × 10-121
< 0.1%
6.7032 × 10-121
< 0.1%
6.694723 × 10-121
< 0.1%
6.6253107 × 10-121
< 0.1%

Interactions

2023-02-24T16:42:15.905270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:00.671839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:02.529844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:04.337013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:06.213995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:08.044103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:09.867229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:11.788092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:13.638147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:16.100742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:00.888233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:02.728314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:04.548447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:06.421441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:08.248557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:10.079662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:11.998530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:13.843598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:16.292237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:01.088698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:02.921796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:04.748912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:06.619910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:08.455003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:10.285140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:12.200990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:14.041071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:16.496684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:01.300134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:03.131237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:04.958352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:06.827355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:08.663449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:10.519487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:12.415414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:14.690516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:16.690168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:01.507577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:03.331701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:05.169787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:07.023830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:08.860921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:10.729922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:12.619869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:14.894969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:16.879663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:01.709042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:03.527207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:05.374239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:07.220307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:09.054402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:10.933378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:12.827314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:15.095427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:17.085108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:01.927457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:03.737615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:05.596648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:07.438748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:09.267831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:11.154788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:13.037752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:15.313815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:17.275569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:02.129916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:03.937083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:05.807082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:07.642177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:09.468295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:11.366220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:13.238216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:15.512285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:17.470050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:02.332372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:04.135551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:06.011536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:07.844635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:09.665768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:11.579651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:13.439679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:42:15.710785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T16:42:21.906960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diff
400kmDensity1.000-0.3230.3310.7590.8080.8390.8200.8470.052
SYM/H_INDEX_nT-0.3231.000-0.520-0.209-0.212-0.214-0.217-0.208-0.007
1-M_AE_nT0.331-0.5201.0000.2500.2570.2660.2410.2700.007
DAILY_SUNSPOT_NO_0.759-0.2090.2501.0000.9310.9060.9110.8900.009
DAILY_F10.7_0.808-0.2120.2570.9311.0000.9440.9380.9370.009
SOLAR_LYMAN-ALPHA_W/m^20.839-0.2140.2660.9060.9441.0000.9650.9910.009
mg_index (core to wing ratio (unitless))0.820-0.2170.2410.9110.9380.9651.0000.9550.008
irradiance (W/m^2/nm)0.847-0.2080.2700.8900.9370.9910.9551.0000.009
d_diff0.052-0.0070.0070.0090.0090.0090.0080.0091.000
2023-02-24T16:42:22.079530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3530.2530.7020.3120.7900.7720.802NaNNaN0.065
SYM/H_INDEX_nT-0.3531.000-0.507-0.177-0.052-0.183-0.184-0.179NaNNaN0.001
1-M_AE_nT0.253-0.5071.0000.1860.0690.2040.1810.211NaNNaN0.000
DAILY_SUNSPOT_NO_0.702-0.1770.1861.0000.3710.8970.9030.875NaNNaN-0.000
DAILY_F10.7_0.312-0.0520.0690.3711.0000.3930.3810.387NaNNaN-0.000
SOLAR_LYMAN-ALPHA_W/m^20.790-0.1830.2040.8970.3931.0000.9670.987NaNNaN-0.001
mg_index (core to wing ratio (unitless))0.772-0.1840.1810.9030.3810.9671.0000.954NaNNaN-0.001
irradiance (W/m^2/nm)0.802-0.1790.2110.8750.3870.9870.9541.000NaNNaN-0.001
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
d_diff0.0650.0010.000-0.000-0.000-0.001-0.001-0.001NaNNaN1.000
2023-02-24T16:42:22.259046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3230.3310.7590.8080.8390.8200.847NaNNaN0.052
SYM/H_INDEX_nT-0.3231.000-0.520-0.209-0.212-0.214-0.217-0.208NaNNaN-0.007
1-M_AE_nT0.331-0.5201.0000.2500.2570.2660.2410.270NaNNaN0.007
DAILY_SUNSPOT_NO_0.759-0.2090.2501.0000.9310.9060.9110.890NaNNaN0.009
DAILY_F10.7_0.808-0.2120.2570.9311.0000.9440.9380.937NaNNaN0.009
SOLAR_LYMAN-ALPHA_W/m^20.839-0.2140.2660.9060.9441.0000.9650.991NaNNaN0.009
mg_index (core to wing ratio (unitless))0.820-0.2170.2410.9110.9380.9651.0000.955NaNNaN0.008
irradiance (W/m^2/nm)0.847-0.2080.2700.8900.9370.9910.9551.000NaNNaN0.009
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
d_diff0.052-0.0070.0070.0090.0090.0090.0080.009NaNNaN1.000
2023-02-24T16:42:22.440533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2220.2240.5630.6040.6370.6140.648NaNNaN0.037
SYM/H_INDEX_nT-0.2221.000-0.367-0.145-0.145-0.145-0.147-0.141NaNNaN-0.005
1-M_AE_nT0.224-0.3671.0000.1700.1730.1780.1610.180NaNNaN0.005
DAILY_SUNSPOT_NO_0.563-0.1450.1701.0000.7880.7370.7440.715NaNNaN0.006
DAILY_F10.7_0.604-0.1450.1730.7881.0000.7970.7840.784NaNNaN0.006
SOLAR_LYMAN-ALPHA_W/m^20.637-0.1450.1780.7370.7971.0000.8350.923NaNNaN0.006
mg_index (core to wing ratio (unitless))0.614-0.1470.1610.7440.7840.8351.0000.808NaNNaN0.005
irradiance (W/m^2/nm)0.648-0.1410.1800.7150.7840.9230.8081.000NaNNaN0.006
stormNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
d_diff0.037-0.0050.0050.0060.0060.0060.0050.006NaNNaN1.000
2023-02-24T16:42:22.616091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diff
400kmDensity1.0000.6170.1950.5920.4790.6570.6440.6660.230
SYM/H_INDEX_nT0.6171.0000.4860.3150.4230.2790.2830.2890.135
1-M_AE_nT0.1950.4861.0000.2320.0500.2400.2240.2350.087
DAILY_SUNSPOT_NO_0.5920.3150.2321.0000.6420.8910.8910.8670.104
DAILY_F10.7_0.4790.4230.0500.6421.0000.6480.6320.6110.058
SOLAR_LYMAN-ALPHA_W/m^20.6570.2790.2400.8910.6481.0000.9630.9750.151
mg_index (core to wing ratio (unitless))0.6440.2830.2240.8910.6320.9631.0000.9370.128
irradiance (W/m^2/nm)0.6660.2890.2350.8670.6110.9750.9371.0000.140
d_diff0.2300.1350.0870.1040.0580.1510.1280.1401.000

Missing values

2023-02-24T16:42:17.617656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T16:42:18.119344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
41666481.379810e-12-25.0304.029.090.40.0064760.2661300.00528712-7.327200e-14
15801735.446754e-12-12.0493.016.0109.40.0074260.2707500.006273122.235850e-13
8689451.905996e-12-12.0374.00.090.80.0071430.2686300.00586212-5.992100e-14
43802392.101438e-12-24.0113.026.087.00.0068840.2680100.00558012-9.966000e-14
39311923.935210e-134.035.00.069.30.0060230.2639600.004955122.666200e-14
4774293.494416e-12-42.0598.061.0119.40.0078440.2721900.00608412-2.764100e-14
4659092.780939e-12-2.023.066.0105.40.0078130.2711900.006188129.328600e-14
14739971.854221e-12-11.0392.0154.0148.70.0080550.2734100.006299126.382400e-14
36147452.842152e-12-31.0335.057.093.40.0069400.2690500.005842121.463760e-13
11090031.718480e-12-35.0271.083.0119.20.0070870.2728260.005679122.151000e-15
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
30131532.665953e-12-37.0608.056.0112.90.0069360.2696400.005529128.310000e-16
21696831.719291e-12-24.085.0143.0151.60.0078200.2773200.006188127.015600e-14
9949851.038993e-12-68.0371.068.094.40.0071140.2703000.005596121.744400e-14
7663332.896571e-12-26.0526.063.0105.50.0072730.2709800.00579712-4.529000e-15
21151822.579274e-12-11.0102.094.0121.40.0075330.2741260.006080122.953350e-13
39971783.204385e-12-52.0115.058.0111.10.0072230.2694200.005815121.134720e-13
5836211.730977e-12-13.0253.060.098.10.0069730.2689600.005689121.477350e-13
21582096.986240e-13-20.0227.014.074.40.0063380.2648200.005156125.440910e-14
30320944.865544e-13-1.0454.024.074.00.0063920.2655800.00524012-7.441650e-14
35237785.486785e-13-21.0332.00.067.10.0060620.2646840.004969127.078760e-14